README.Rmd

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output: github_document
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
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# IntCens

The `IntCens` package provides non- and semi-parametric methods for analyzing interval-censored data. This includes iterative estimation routines for proportional hazards and proportional odds models, as well as nonparametric maximum likelihood estimation.

## Installation

You can install the development version of IntCens from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("lmaowisc/IntCens")
```

## Example

The main function for fitting nonparametric, PH, and PO models is `icsurvfit()`.
Below is a basic example:
```{r example}
library(IntCens)
## basic example code
# A dataset containing interval-censored observations and a treatment indicator 
# (radiation vs. radiation plus chemotherapy)
data(bcos)
n <- nrow(bcos) # sample size
# Extract the left and right endpoints of the intervals
set.seed(123)
L <- bcos$left + rnorm(n, 0, 0.0001) # add random noise
R <- bcos$right + rnorm(n, 0, 0.0001)
# Covariate (binary)
Z <- as.numeric(bcos$treatment == "RadChem")

# Nonparametric
obj_np <- icsurvfit(L, R)
plot(obj_np, main = "Nonparametric")

# Cox model (PH)
obj_ph <- icsurvfit(L, R, Z, model = "PH")
obj_ph

# Proportional odds model (PO)
obj_po <- icsurvfit(L, R, Z, model = "PO")
obj_po
```